arXiv:2604.27292v3 Announce Type: replace Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed AI systems, these boundaries are defined independently, creating three regions: governed capabilities (the only useful region), ungoverned capabilities (risk), and governance policies that address non-existent capabilities (theater). Two of the three regions are failure modes. We focus on the governance of effects: actions that AI systems perform in the world (API calls, database writes, tool i
The increasing deployment of AI systems with real-world effects highlights critical gaps in current governance approaches, making the inherent structural failures of behavioral AI governance more apparent.
This paper reveals that current AI governance often addresses non-existent capabilities while failing to cover actual risks, wasting resources and creating dangerous blind spots for deployed AI.
The focus shifts from merely defining AI capabilities or governance policies independently to understanding the critical overlap and misalignment between what AI can do and what is actually governed.
- · AI governance frameworks with clear 'effect' boundaries
- · Developers of auditable and transparent AI systems
- · Regulators adopting adaptive, capability-aware policies
- · Organizations relying on 'theater' governance policies
- · AI systems with ambiguous effect boundaries
- · Policy makers ignoring direct AI system capabilities
Increased scrutiny on the gap between stated AI governance and actual AI system capabilities, especially concerning actions in the real world.
A push for more integrated design-time governance mechanisms that align AI system expressiveness with control parameters to minimize ungoverned risks.
The emergence of new regulatory bodies or methodologies specifically designed to audit and certify the 'governance interface' of AI systems, similar to safety-critical software engineering.
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Read at arXiv cs.AI